Patients with NSAIDs using Eunomia: an exploratory analysis
Author
Carmen Ng
Executive summary: NSAID Drug Initiation Analysis
Conducted exploratory analysis on patients initiating NSAIDs using synthetic OMOP CDM v5.4 database. NSAID cohort defined as Cohort_Definition_ID=4.
Key aspects analyzed:
Demographics:
Age and gender distribution
Baseline drug use
Baseline conditions
Post-index outcomes, particularly GI bleeding (Condition Concept ID 192671)
Methodology:
Used Quarto for dynamic reporting
Created visual representations for clarity
Organized code and documentation in GitHub repository
Analysis provides insights into NSAID initiation patterns and potential outcomes, contributing to healthcare analytics discussions on NSAID safety.
Cohorts created in table main.cohort
cohortId name
1 1 Celecoxib
2 2 Diclofenac
3 3 GiBleed
4 4 NSAIDs
description
1 A simplified cohort definition for new users of celecoxib, designed specifically for Eunomia.
2 A simplified cohort definition for new users ofdiclofenac, designed specifically for Eunomia.
3 A simplified cohort definition for gastrointestinal bleeding, designed specifically for Eunomia.
4 A simplified cohort definition for new users of NSAIDs, designed specifically for Eunomia.
count
1 1844
2 850
3 479
4 2694
Study population
Expand for full code
sql <-"SELECT * FROM main.cohort WHERE COHORT_DEFINITION_ID = 4;"NSAIDs <-querySql(connection = connection, sql = sql) |>as.data.frame()query_demo <-"SELECT person.*, concept.concept_name AS gender FROM person LEFT JOIN concept AS concept ON person.GENDER_CONCEPT_ID=concept.CONCEPT_IDWHERE person.person_id IN ( SELECT subject_id FROM main.cohort WHERE COHORT_DEFINITION_ID = 4);"demo <-querySql(connection = connection, sql = query_demo) |>as.data.frame()demo <- demo %>%left_join(NSAIDs, by=c("PERSON_ID"="SUBJECT_ID")) %>%mutate(age =as.numeric(as.Date(COHORT_START_DATE)-as.Date(BIRTH_DATETIME))/365.25)comma <-function(x) format(x, digits =2, big.mark =",")
There are 2,694 patients in the cohort who initiated NSAID use.
Table 1. Characteristics of patients using NSAIDs
Expand for full code
x <-table1(~age+RACE_SOURCE_VALUE| GENDER, data=demo,extra.col=list(`P-value`=pvalue), extra.col.pos=3)
Warning in chisq.test(table(y, g)): Chi-squared approximation may be incorrect
Expand for full code
x %>%kbl() %>%kable_styling(bootstrap_options =c("striped", "hover"))
FEMALE
MALE
P-value
Overall
(N=1373)
(N=1321)
(N=2694)
age
Mean (SD)
38.7 (3.34)
38.6 (3.18)
0.309
38.6 (3.26)
Median [Min, Max]
38.6 [31.0, 46.6]
38.5 [31.5, 46.7]
38.6 [31.0, 46.7]
RACE_SOURCE_VALUE
asian
112 (8.2%)
100 (7.6%)
0.665
212 (7.9%)
black
175 (12.7%)
163 (12.3%)
338 (12.5%)
hispanic
214 (15.6%)
221 (16.7%)
435 (16.1%)
native
6 (0.4%)
8 (0.6%)
14 (0.5%)
other
2 (0.1%)
0 (0%)
2 (0.1%)
white
864 (62.9%)
829 (62.8%)
1693 (62.8%)
Drug use at baseline
Expand for full code
query_drug <-"SELECT de.*, coh.cohort_start_date, c.concept_name AS drug_nameFROM drug_era deJOIN main.cohort coh ON de.person_id = coh.subject_idLEFT JOIN observation_period AS op ON coh.subject_id=op.person_idLEFT JOIN concept AS c ON de.drug_concept_id=c.concept_idWHERE coh.cohort_definition_id = 4 AND de.drug_era_end_date <= coh.cohort_start_date;"drug <-querySql(connection = connection, sql = query_drug) |>as.data.frame()drug_n <- drug %>%distinct(PERSON_ID, DRUG_NAME) %>%group_by(PERSON_ID) %>%summarise(count =n())
Our analysis has provided valuable insights into NSAID initiation and its correlation with GI bleeding. However, to gain a more comprehensive understanding, I propose further investigation into the following areas:
Drug Strength and Duration of Use: Analyze the impact of NSAID dosage and treatment length on GI bleeding risk, including time-to-event analysis.
Adherence to Treatment: Evaluate patient compliance using pharmacy refill data and its relationship to adverse events.
Specific NSAID types: Compare the risk profiles of different NSAID types (e.g., ibuprofen, naproxen, diclofenac) to identify if certain drugs carry higher risks.
Healthcare utilization patterns: Examine how factors like frequency of doctor visits or hospitalizations correlate with NSAID use and GI bleeding events.
Long-term outcomes: Conduct a longitudinal study to assess long-term consequences of NSAID use and GI bleeding events on patient health and quality of life.
These additional analyses will provide a deeper understanding of NSAID-related risks and inform strategies to enhance patient safety and optimize prescribing practices.